OpenRaiser/Intern-Atlas huggingface dataset:
Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we study extensions to the Gaussian Processes (GPs) continuous occupancy mapping problem. There are two classes of occupancy mapping problems that we particularly investigate. The first problem is related to mapping under pose uncertainty and how to propagate pose estimation uncertainty into the map inference. We develop expected kernel and expected sub-map notions to deal with uncertain inputs. In the second problem, we account for the complication of the robot's perception noise using Warped Gaussian Processes (WGPs). This approach allows for non-Gaussian noise in the observation space and captures the possible nonlinearity in that space better than standard GPs. The developed techniques can be applied separately or concurrently to a standard GP occupancy mapping problem. According to our experimental results, although taking into account pose uncertainty leads, as expected, to more uncertain maps, by modeling the nonlinearities present in the observation space WGPs improve the map quality.
Missing measurements on RIPE Atlas
Venue: arXiv cs.NI 2017
Year: 2017

We show that it is common to lose some datapoints for mea-surements scheduled at regular interval on RIPE Atlas. Thetemporal correlation between missing measurements and con-nection events are analyzed, in the pursuit of understandingreasons behind such missings. To our surprise, a big part of measurements are lost while probes are connected.
RADIUS: A System for Detecting Anomalous Link Quality Degradation in Wireless Sensor Networks
Venue: arXiv cs.NI 2017
Year: 2017

To ensure proper functioning of a Wireless Sensor Network (WSN), it is crucial that the network is able to detect anomalies in communication quality (e.g., RSSI), which may cause performance degradation, so that the network can react accordingly. In this paper, we introduce RADIUS, a lightweight system for the purpose. The design of RADIUS is aimed at minimizing the detection error (caused by normal randomness of RSSI) in discriminating good links from weak links and at reaching high detection accuracy under diverse link conditions and dynamic environment changes. Central to the design is a threshold-based decision approach that has its foundation on the Bayes decision theory. In RADIUS, various techniques are developed to address challenges inherent in applying this approach. In addition, through extensive experiments, proper configuration of the parameters involved in these techniques is identified for an indoor environment. In a prototype implementation of the RADIUS system deployed in an indoor testbed, the results show that RADIUS is accurate in detecting anomalous link quality degradation for all links across the network, maintaining a stable error rate of 6.13% on average.
Path-following based Point Matching using Similarity Transformation
Venue: arXiv cs.CV 2017
Year: 2017

To address the problem of 3D point matching where the poses of two point sets are unknown, we adapt a recently proposed path following based method to use similarity transformation instead of the original affine transformation. The reduced number of transformation parameters leads to more constrained and desirable matching results. Experimental results demonstrate better robustness of the proposed method over state-of-the-art methods.
Stochastic Planning and Lifted Inference
Venue: arXiv cs.AI 2017
Year: 2017

Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming for lifted stochastic planning (Boutilier et al, 2001) were introduced around the same time as algorithmic efforts to use abstraction in stochastic systems. Over the years, these ideas evolved into two distinct lines of research, each supported by a rich literature. Lifted probabilistic inference focused on efficient arithmetic operations on template-based graphical models under a finite domain assumption while symbolic dynamic programming focused on supporting sequential decision-making in rich quantified logical action models and on open domain reasoning. Given their common motivation but different focal points, both lines of research have yielded highly complementary innovations. In this chapter, we aim to help close the gap between these two research areas by providing an overview of lifted stochastic planning from the perspective of probabilistic inference, showing strong connections to other chapters in this book. This also allows us to define Generalized Lifted Inference as a paradigm that unifies these areas and elucidates open problems for future research that can benefit both lifted inference and stochastic planning.
New descriptions of the weighted Reed-Muller codes and the homogeneous Reed-Muller codes
Venue: arXiv cs.IT 2017
Year: 2017

We give a description of the weighted Reed-Muller codes over a prime field in a modular algebra. A description of the homogeneous Reed-Muller codes in the same ambient space is presented for the binary case. A decoding procedure using the Landrock-Manz method is developed.
Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing. A major reason for this is the limited amount of annotated training data. In this paper, we propose the idea of leveraging the discriminative power of pre-trained deep CNNs on 2-dimensional sensor data by transforming the sensor modality to the visual domain. By three proposed strategies, 2D sensor output is converted into pressure distribution imageries. Then we utilize a pre-trained CNN for transfer learning on the converted imagery data. We evaluate our method on a gait dataset of floor surface pressure mapping. We obtain a classification accuracy of 87.66%, which outperforms the conventional machine learning methods by over 10%.
Approximate Value Iteration for Risk-aware Markov Decision Processes
Venue: arXiv cs.SY 2017
Year: 2017

We consider large-scale Markov decision processes (MDPs) with a risk measure of variability in cost, under the risk-aware MDPs paradigm. Previous studies showed that risk-aware MDPs, based on a minimax approach to handling risk, can be solved using dynamic programming for small to medium sized problems. However, due to the "curse of dimensionality", MDPs that model real-life problems are typically prohibitively large for such approaches. In this paper, we employ an approximate dynamic programming approach, and develop a family of simulation-based algorithms to approximately solve large-scale risk-aware MDPs. In parallel, we develop a unified convergence analysis technique to derive sample complexity bounds for this new family of algorithms.
Unconstrained inverse quadratic programming problem
Venue: arXiv cs.NA 2017
Year: 2017

The paper covers a formulation of the inverse quadratic programming problem in terms of unconstrained optimization where it is required to find the unknown parameters (the matrix of the quadratic form and the vector of the quasi-linear part of the quadratic form) provided that approximate estimates of the optimal solution of the direct problem and those of the target function to be minimized in the form of pairs of values lying in the corresponding neighborhoods are only known. The formulation of the inverse problem and its solution are based on the least squares method. In the explicit form the inverse problem solution has been derived in the form a system of linear equations. The parameters obtained can be used for reconstruction of the direct quadratic programming problem and determination of the optimal solution and the extreme value of the target function, which were not known formerly. It is possible this approach opens new ways in over applications, for example, in neurocomputing and quadric surfaces fitting. Simple numerical examples have been demonstrated. A scenario in the Octave/MATLAB programming language has been proposed for practical implementation of the method.
MDS-Coded Distributed Caching for Low Delay Wireless Content Delivery
Venue: arXiv cs.IT 2017
Year: 2017

We investigate the use of maximum distance separable (MDS) codes to cache popular content to reduce the download delay of wireless content delivery. In particular, we consider a cellular system where devices roam in an out of a cell according to a Poisson random process. Popular content is cached in a limited number of the mobile devices using an MDS code and can be downloaded from the mobile devices using device-to-device communication. We derive an analytical expression for the delay incurred in downloading content from the wireless network and show that distributed caching using MDS codes can dramatically reduce the download delay with respect to the scenario where content is always downloaded from the base station and to the case of uncoded distributed caching.
VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement
Venue: arXiv cs.MM 2017
Year: 2017

A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed. Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-second sequences in four resolutions (i.e., $1920 \times 1080$, $1280 \times 720$, $960 \times 540$ and $640 \times 360$). For each of the 880 video clips, we encode it using the H.264 codec with $QP=1, \cdots, 51$ and measure the first three JND points with 30+ subjects. The dataset is called the "VideoSet", which is an acronym for "Video Subject Evaluation Test (SET)". This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort.
On the Reliability Function of the Common-Message Broadcast Channel with Variable-Length Feedback
Venue: arXiv cs.IT 2017
Year: 2017

We derive upper and lower bounds on the reliability function for the common-message discrete memoryless broadcast channel with variable-length feedback. We show that the bounds are tight when the broadcast channel is stochastically degraded. For the achievability part, we adapt Yamamoto and Itoh's coding scheme by controlling the expectation of the maximum of a set of stopping times. For the converse part, we adapt Burnashev's proof techniques for establishing the reliability functions for (point-to-point) discrete memoryless channels with variable-length feedback and sequential hypothesis testing.
Suboptimum Low Complexity Joint Multi-target Detection and Localization for Noncoherent MIMO Radar with Widely Separated Antennas
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, the problems of simultaneously detecting and localizing multiple targets are considered for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming a prior knowledge of target number, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching parameters of interest in a high dimensional space. However, the complexity of this method increases exponentially with the number G of targets.Besides, without the prior information of the number of targets, a multi-hypothesis testing strategy to determine the number of targets is required, which further complicates this method. Therefore, we split the joint maximization into G disjoint optimization problems by clearing the interference from previously declared targets. In this way, we derive two fast and robust suboptimal solutions which allow trading performance for a much lower implementation complexity which is almost independent of the number of targets. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show the proposed algorithms can correctly detect and accurately localize multiple targets even when targets share common range bins in some paths.
Enumeration of Extractive Oracle Summaries
Venue: arXiv cs.CL 2017
Year: 2017

To analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of ROUGE-N. We also propose an algorithm that enumerates all of the oracle summaries for a set of reference summaries to exploit F-measures that evaluate which system summaries contain how many sentences that are extracted as an oracle summary. Our experimental results obtained from Document Understanding Conference (DUC) corpora demonstrated the following: (1) room still exists to improve the performance of extractive summarization; (2) the F-measures derived from the enumerated oracle summaries have significantly stronger correlations with human judgment than those derived from single oracle summaries.
Reducing Competitive Cache Misses in Modern Processor Architectures
Venue: arXiv cs.AR 2017
Year: 2017

The increasing number of threads inside the cores of a multicore processor, and competitive access to the shared cache memory, become the main reasons for an increased number of competitive cache misses and performance decline. Inevitably, the development of modern processor architectures leads to an increased number of cache misses. In this paper, we make an attempt to implement a technique for decreasing the number of competitive cache misses in the first level of cache memory. This technique enables competitive access to the entire cache memory when there is a hit - but, if there are cache misses, memory data (by using replacement techniques) is put in a virtual part given to threads, so that competitive cache misses are avoided. By using a simulator tool, the results show a decrease in the number of cache misses and performance increase for up to 15%. The conclusion that comes out of this research is that cache misses are a real challenge for future processor designers, in order to hide memory latency.
On the Usage of Databases of Educational Materials in Macedonian Education
Venue: arXiv cs.CY 2017
Year: 2017

Technologies have become important part of our lives. The steps for introducing ICTs in education vary from country to country. The Republic of Macedonia has invested with a lot in installment of hardware and software in education and in teacher training. This research was aiming to determine the situation of usage of databases of digital educational materials and to define recommendation for future improvements. Teachers from urban schools were interviewed with a questionnaire. The findings are several: only part of the interviewed teachers had experience with databases of educational materials; all teachers still need capacity building activities focusing exactly on the use and benefits from databases of educational materials; preferably capacity building materials to be in Macedonian language; technical support and upgrading of software and materials should be performed on a regular basis. Most of the findings can be applied at both national and international level - with all this implemented, application of ICT in education will have much bigger positive impact.
SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach
Venue: arXiv cs.NI 2017
Year: 2017

Cyber-Physical Systems (CPS) revolutionize various application domains with integration and interoperability of networking, computing systems, and mechanical devices. Due to its scale and variety, CPS faces a number of challenges and opens up a few research questions in terms of management, fault-tolerance, and scalability. We propose a software-defined approach inspired by Software-Defined Networking (SDN), to address the challenges for a wider CPS adoption. We thus design a middleware architecture for the correct and resilient operation of CPS, to manage and coordinate the interacting devices centrally in the cyberspace whilst not sacrificing the functionality and performance benefits inherent to a distributed execution.
Spotting Information biases in Chinese and Western Media
Venue: arXiv cs.IR 2017
Year: 2017

Newswire and Social Media are the major sources of information in our time. While the topical demographic of Western Media was subjects of studies in the past, less is known about Chinese Media. In this paper, we apply event detection and tracking technology to examine the information overlap and differences between Chinese and Western - Traditional Media and Social Media. Our experiments reveal a biased interest of China towards the West, which becomes particularly apparent when comparing the interest in celebrities.
SOI RF Switch for Wireless Sensor Network
Venue: arXiv cs.OH 2017
Year: 2017

The objective of this research was to design a 0-5 GHz RF SOI switch, with 0.18um power Jazz SOI technology by using Cadence software, for health care applications. This paper introduces the design of a RF switch implemented in shunt-series topology. An insertion loss of 0.906 dB and an isolation of 30.95 dB were obtained at 5 GHz. The switch also achieved a third order distortion of 53.05 dBm and 1 dB compression point reached 50.06dBm. The RF switch performance meets the desired specification requirements.
Low Power SI Class E Power Amplifier and RF Switch For Health Care
Venue: arXiv cs.OH 2017
Year: 2017

This research was to design a 2.4 GHz class E Power Amplifier (PA) for health care, with 0.18um Semiconductor Manufacturing International Corporation CMOS technology by using Cadence software. And also RF switch was designed at cadence software with power Jazz 180nm SOI process. The ultimate goal for such application is to reach high performance and low cost, and between high performance and low power consumption design. This paper introduces the design of a 2.4GHz class E power amplifier and RF switch design. PA consists of cascade stage with negative capacitance. This power amplifier can transmit 16dBm output power to a 50Ω load. The performance of the power amplifier and switch meet the specification requirements of the desired.
A Concurrent Model for Imperative Languages with Improved Atomicity
Venue: arXiv cs.PL 2017
Year: 2017

We propose a new concurrent model for imperative languages where concurrency occurs at a subprogram level. This model introduces a new {\it block sequential} statement of the form $#(G_1,\ldots,G_n)$ where each $G_i$ is a statement. This statement tells the machine to execute $G_1,\ldots,G_n$ sequentially and atomically (\ie, without interleaving). It therefore enhances atomicity and predictability in concurrent programming. We illustrate our idea via $C^{\|}$, an extension of the core concurrent C with the new block sequential statement.
Structural Attention Neural Networks for improved sentiment analysis
Venue: arXiv cs.CL 2017
Year: 2017

We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree. To our knowledge, the proposed models achieve state of the art performance on the Stanford Sentiment Treebank dataset.
Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based representations enable us to fine-tune the existing ConvNets models trained on image data for classification of depth sequences, without introducing large parameters to learn. Upon the proposed representations, a convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The method achieved 55.57\% classification accuracy and ranked $2^{nd}$ place in this challenge but was very close to the best performance even though we only used depth data.
Modeling Actuation Constraints for IoT Applications
Venue: arXiv cs.SY 2017
Year: 2017

Internet of Things (IoT) promises to bring ease of monitoring, better efficiency and innovative services across many domains with connected devices around us. With information from critical parts of infrastructure and powerful cloud-based data analytics, many applications can be developed to gain insights about IoT systems as well as transform their capabilities. Actuation applications form an essential part of these IoT systems, as they enable automation as well as fast low-level decision making. However, modern IoT systems are designed for data acquisition, and actuation applications are implemented in an ad-hoc manner. We identify modeling constraints in a systematic manner as indispensable to support actuation applications because constraints encompass high-level policies dictated by laws of physics, legal policies, user preferences. We explore data models for constraints inIoT system with the example of a home heating system and illustrate the challenges in enforcing these constraints in theIoT system architecture.
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS
Venue: arXiv cs.LG 2017
Year: 2017

The Intelligent Transportation System (ITS) targets to a coordinated traffic system by applying the advanced wireless communication technologies for road traffic scheduling. Towards an accurate road traffic control, the short-term traffic forecasting to predict the road traffic at the particular site in a short period is often useful and important. In existing works, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular approach. The scheme however encounters two challenges: 1) the analysis on related data is insufficient whereas some important features of data may be neglected; and 2) with data presenting different features, it is unlikely to have one predictive model that can fit all situations. To tackle above issues, in this work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we first explore the autocorrelation and distribution features existed in traffic flow to revise structure of the time series model. Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA. We show the efficiency and accuracy of our proposal using both analysis and experimental studies. Using the real-world trace data, we show that the proposed predicting approach can achieve satisfactory performance in practice.